10
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Improving Subseasonal Forecasting in the Western U.S. with Machine Learning

      Preprint
      , , , ,

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Water managers in the western United States (U.S.) rely on longterm forecasts of temperature and precipitation to prepare for droughts and other wet weather extremes. To improve the accuracy of these longterm forecasts, the Bureau of Reclamation and the National Oceanic and Atmospheric Administration (NOAA) launched the Subseasonal Climate Forecast Rodeo, a year-long real-time forecasting challenge, in which participants aimed to skillfully predict temperature and precipitation in the western U.S. two to four weeks and four to six weeks in advance. Here we present and evaluate our machine learning approach to the Rodeo and release our SubseasonalRodeo dataset, collected to train and evaluate our forecasting system. Our system is an ensemble of two regression models. The first integrates the diverse collection of meteorological measurements and dynamic model forecasts in the SubseasonalRodeo dataset and prunes irrelevant predictors using a customized multitask model selection procedure. The second uses only historical measurements of the target variable (temperature or precipitation) and introduces multitask nearest neighbor features into a weighted local linear regression. Each model alone is significantly more accurate than the operational U.S. Climate Forecasting System (CFSv2), and our ensemble skill exceeds that of the top Rodeo competitor for each target variable and forecast horizon. We hope that both our dataset and our methods will serve as valuable benchmarking tools for the subseasonal forecasting problem.

          Related collections

          Most cited references5

          • Record: found
          • Abstract: not found
          • Article: not found

          An All-Season Real-Time Multivariate MJO Index: Development of an Index for Monitoring and Prediction

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Development of a Human–Machine Mix for Forecasting Severe Convective Events

              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Application of Statistical Models to the Prediction of Seasonal Rainfall Anomalies over the Sahel

                Bookmark

                Author and article information

                Journal
                19 September 2018
                Article
                1809.07394
                19c3a775-45f0-433f-b7d4-52118a5a1713

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                stat.AP cs.CY stat.ML

                Applied computer science,Applications,Machine learning
                Applied computer science, Applications, Machine learning

                Comments

                Comment on this article